Sexual, Social, and Genetic Network Overlap: A Socio-Molecular Approach Toward Public Health Intervention of HIV.

Abstract

To determine how network-level factors influence individual risk of HIV acquisition, which is key in preventing disease transmission. We recruited a cohort of young Black men who have sex with men (n = 618) in Chicago, Illinois, from 2013 to 2016. We identified potential molecular ties via pairwise genetic distance analysis of HIV pol sequences with links inferred between individuals whose sequences were 1.5% or less genetically distant. We defined clusters as 1 or more connections to another individual. We conducted entity resolution between confidant, sexual, referral, and Facebook network data between network types. Of 266 (43.0%) participants identified as HIV-positive, we obtained 86 (32.3%) genetic sequences. Of these, 35 (40.7%) were linked to 1 or more other sequence; however, none of these were identified in first-, second-, or third-degree confidant and sexual networks. Minimal overlap existed between genetic and Facebook ties. These results suggest that HIV transmissions may have occurred before elicitation of network data; future studies should expand the data collection timeframe to more accurately determine risk networks. Virtual network data, such as Facebook, may be particularly useful in developing one's risk environment.